The greatest barrier to the widespread impact of predictive analytics in higher education is adoption. No matter how great the technology is, if people don’t use it effectively, any potential value is lost.

In the early stages of predictive analytics implementations at colleges and universities, a common obstacle comes in the form of questions that arise from some essential misunderstandings about data science and predictive analytics. Without a clear understanding of what predictive analytics are, how they work, and what they do, it is easy to establish false expectations. When predictive analytics fail to live up to these expectations, the result is disappointment, frustration, poor adoption, and a failure to fully actualize their potential value for student success.

This post is the first in a series of posts addressing common misunderstandings about data science that can have serious consequences for the success of an educational data or learning analytics analytics initiative in higher education. The most basic misunderstanding that people have is about the language of prediction. What do we mean by ‘predictive’ analytics, anyway?

Why is the concept of ‘Predictive Analytics’ so confusing?

The term ‘predictive analytics’ is used widely, not just in education, but across all knowledge domains. We use the term because everyone else uses it, but it is actually pretty misleading.

I have written about this at length elsewhere, but in nutshell the term ‘prediction’ has a long history of being associated with a kind of mystical access to true knowledge about future events in a deterministic universe. The history of the term is important, because it explains why many people get hung up on issues of accuracy, as if the goal of predictive analytics was to become something akin to the gold standard of a crystal ball. It also explains why others are immediately creeped out by conversations about predictive analytics in higher education, because the term ‘prediction’ carries with it a set of pretty heavy metaphysical and epistemological connotations. It is not uncommon in discussions of ethics and AI in higher education to hear comparisons between predictive analytics and the world of the film Minority Report (which is awesome), in which government agents are able to intervene and arrest people for crimes before they were committed. In these conversations, however, it is rarely remembered that Minority Report predictions were quasi-magical in origin, where predictive analytics involve computational power applied to incomplete information.

Predictive analytics are not magic, even if the language of prediction sets us up to think of it in this way. In The Signal an the Noise, Nate Silver suggests that we can begin to overcome this confusion by using the language of forecasting instead. Where the goal of prediction is to be correct, the goal of a forecast is to be prepared. I watch the weather channel, not because I want to know what the weather is going to be like, but because I want to know whether I need to pack an umbrella.

In higher education, it is unlikely that we will stop talking about predictive analytics any time soon. But it is important to shift our thinking and set our expectations along the lines of forecasting. When it comes to the early identification of at-risk students, our aim is not to be 100% accurate, and we are not making deterministic claims about a particular student’s future behavior. What we are doing is providing a forecast based on incomplete information about groups of students in the past so that instructors and professional advisors can take action. The goal of predictive analytics in higher education is to offer students an umbrella when the sky turns grey and there is a strong chance of rain.

For the last month, I have been tracking the terms “Product Marketing” and “Product Marketer” using Google alerts. In that time, except for a few exceptions, all I have see are job advertisements. A LOT of job advertisements. For a position that is in such high demand, the fact that there is so little written about it is remarkable indeed.

So, what is product marketing? It’s complicated.

It is commonly accepted that product marketing exists at the intersection of marketing, product management, and sales. A product marketer ‘owns’ messaging for a product or product line. In support of field and central marketing, they work to ensure that what a product ‘means’ is coherent, consistent with broader corporate messaging and brand standards, and compelling to a full range of buying personas. The messaging produced by a product marketer comes to life in two forms: through outward-facing collateral used for demand generation, and inward-facing resources used for sales enablement.

So what is a product marketer? They are a story-teller who serves the interests of marketing, product, and sales through the creation of messaging that is coherent, consistent, and compelling.

It would be easy to stop here and think of the product marketer as a person in the present, as someone who creates stories that strike a balance between the three types of organizational interest it serves. Is a product marketer someone who creates messages that ‘work’ here and now? Yes. But if we also take seriously the role of a product marketer in creating, not just meaning, but also vision, then the product marketer also bears a kind of responsibility to the future. And as it turns out, the most effective and impactful product narratives are those that point beyond an immediate need and toward a future in which a thing is not only useful, but also important.

For me, the most exciting part of product marketing is its relationship to product management. This relationship is not one-way. It is not as if product management creates a thing, and then hands it to ‘the marketing guy’ to ‘market.’ To the extent that a product marketer is responsible for what a thing means, they also have a direct impact on what it becomes. With a meaning that is coherent, consistent, and compelling comes an understanding of the problems and needs of the market. It also necessarily defines values. By working with product management to understand, not just what is possible, but also what is meaningful, the product marketer importantly contributes to a vision for a product that is actualized in the form of a roadmap.

If you can’t say something important, don’t say anything at all.

How common is the commitment to importance among product marketers? I can’t say. But I would like to think that a commitment to importance is essential to being an excellent product marketer. It renders the role itself important (as opposed to merely useful). But with importance comes greater responsibility. It means developing domain expertise over and above the general expertise of being a product marketer. With domain expertise comes a greater sense of empathy for the industries your product supports.

The minute that a product marketer shifts their perspective from the present to the future, their locus of responsibility also changes. Focused on the present, the product marketer is an advocate on behalf of the product to the market. Focused on the future, the product marketer serves as an advocate to the product on behalf of the market.

What, then, is a product marketer? They are a story-teller who advocates on behalf of the market to an organization’s marketing, product, and sales departments through the creation of narratives that are coherent, consistent, and compelling.